TOWARD ROBUST STRESS DETECTION: A 1D-CNN APPROACH FOR MULTICLASS ECG CLASSIFICATION UNDER LIMITED DATA CONDITIONS
Abstract
Automated stress detection through physiological signals holds considerable promise for transforming healthcare delivery, reducing associated costs, and enabling timely clinical intervention in stress-related conditions. This paper presents a review of recent developments in stress classification based on physiological data, with emphasis on the most relevant computational methods, prevailing methodological challenges, and emergent research directions in the field. Particular attention is devoted to the constraints imposed by small-scale datasets, the critical role of subject-specific personalized models, and the practical barriers to real-time deployment in naturalistic, uncontrolled settings. Concurrently, we introduce and assess a novel one-dimensional convolutional neural network (CNN) architecture tailored to classify electrocardiogram (ECG) signals across four distinct stress-phase categories. Under data-constrained experimental conditions, the model exhibits robust learning dynamics and adequate generalization, attaining 72.81% accuracy on an independent held-out test set. These outcomes underscore the viability of deep learning for stress classification tasks and highlight the need for future research oriented toward personalized, multimodal, and real-time physiological monitoring frameworks.
Author Biography
PhD in Computer Science from the Universidade Federal do Rio Grande do Sul.
References
ANSARI, Y.; MOURAD, O.; QARAQE, K.; SERPEDIN, E. Deep learning for ECG arrhythmia detection and classification: an overview of progress for 2017–2023. Frontiers in Physiology, Lausanne, v. 14, 2023. DOI: https://doi.org/10.3389/fphys.2023.1246746
ASKARI, M.; SETAREHDAN, S. K.; SHOKROLLAHI, A.; ALIREZAEI, M. Stress detection from physiological signals using deep learning methods: distinguishing stress from physical activity. Biomedical Signal Processing and Control, Amsterdam, v. 73, 2022.
BOBADE, P.; VANI, M. Stress detection with machine learning and deep learning using multimodal physiological data. In: INTERNATIONAL CONFERENCE ON INVENTIVE RESEARCH IN COMPUTING APPLICATIONS (ICIRCA), 2., 2020. Proceedings […]. [S. l.]: IEEE, 2020. p. 51–57. DOI: https://doi.org/10.1109/ICIRCA48905.2020.9183244
CHATTERJEE, R.; NAG, A.; DUTTA, A.; MUKHERJEE, S. Stress classification using physiological signals with frequency-domain feature selection. Biomedical Signal Processing and Control, Amsterdam, v. 72, 2022.
FINSETH, T.; KJELDGAARD, M.; SKOV, M. B.; HANSEN, L. K. Personalized versus generalized models for physiological stress detection: challenges and opportunities. Sensors, Basel, v. 23, n. 3, 2023.
GARG, P.; SANTHOSH, J.; DENGEL, A.; ISHIMARU, S. Stress detection by machine learning and wearable sensors. In: INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES – COMPANION, 26., 2021, New York. Proceedings […]. New York: ACM, 2021. p. 43–45. DOI: https://doi.org/10.1145/3397482.3450732
GEDAM, S.; PAUL, S. A review on mental stress detection using wearable sensors and machine learning techniques. IEEE Access, New York, v. 9, p. 84045–84066, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3085502
GUHDAR, M.; MOHAMMED, A. O.; MSTAFA, R. J. Advanced deep learning framework for ECG arrhythmia classification using 1D-CNN with attention mechanism. Knowledge-Based Systems, Amsterdam, v. 295, 2025. DOI: https://doi.org/10.1016/j.knosys.2025.113301
HUANG, J.; LIU, Y.; PENG, X. Recognition of driver's mental workload based on physiological signals: a comparative study. Biomedical Signal Processing and Control, Amsterdam, v. 71, pt. A, p. 103094, 2022. DOI: https://doi.org/10.1016/j.bspc.2021.103094
KUMAR, A.; SHARMA, K.; SHARMA, A. Hierarchical deep neural network for mental stress state detection using IoT-based biomarkers. Pattern Recognition Letters, Amsterdam, v. 145, p. 81–87, 2021. DOI: https://doi.org/10.1016/j.patrec.2021.01.030
MINGXU, L.; ZHANG, Y.; LIU, H.; WANG, Z. Affect and stress detection based on feature fusion of LSTM and CNN using physiological signals. Sensors, Basel, v. 23, 2023.
MODI, N.; KUMAR, Y.; MEHTA, K.; CHAPLOT, N. Physiological signal-based mental stress detection using hybrid deep learning models. Discover Artificial Intelligence, Cham, v. 5, 2025. DOI: https://doi.org/10.1007/s44163-025-00412-8
RIM, B.; LEE, J.; PARK, J.; KIM, H. Deep learning applications on medical physiological signals: a comprehensive review. Sensors, Basel, v. 20, n. 20, 2020. DOI: https://doi.org/10.3390/s20040969
SAH, S.; DAS, A.; CHOUDHURY, S. StressNet: convolutional neural network framework for personalized stress classification. IEEE Access, New York, v. 10, 2022.
VARGAS-LOPEZ, O.; PEREZ-RAMIREZ, C. A.; VALTIERRA-RODRIGUEZ, M.; YANEZ-BORJAS, J. J.; AMEZQUITA-SANCHEZ, J. P. An explainable machine learning approach based on statistical indexes and SVM for stress detection in automobile drivers using electromyographic signals. Sensors, Basel, v. 21, n. 9, 2021. DOI: https://doi.org/10.3390/s21093155
XIANG, J.-Z.; WANG, Q.-Y.; FANG, Z.-B.; ESQUIVEL, J. A.; SU, Z.-X. A multimodal deep learning approach for stress detection using physiological signals. Frontiers in Physiology, Lausanne, v. 16, 2025.
WU, Y.; ZHANG, J.; LIU, Y.; WANG, J. Transfer learning-based stress detection using physiological signals in daily activities. IEEE Access, New York, v. 9, 2021.
Can, Y. S., Arnrich, B., & Ersoy, C. (2019). Stress detection from physiological signals: A survey. IEEE Access, 7, 161798–161814. https://doi.org/10.1109/ACCESS.2019.2959811
Gjoreski, M., Gjoreski, H., Lutrek, M., & Gams, M. (2017). Continuous stress detection using a wrist device: In laboratory and real life. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(2), 1–20. https://doi.org/10.1145/3090076 DOI: https://doi.org/10.1145/3090076
Kyrou, I., et al. (2025). Deep learning for physiological signal-based stress detection: A survey. IEEE Transactions on Affective Computing, 16(2). DOI: https://doi.org/10.1109/TAFFC.2024.3455371
Liu, Q., Zhang, X., & Huang, M. (2025). A comprehensive review of machine learning and deep learning for stress detection using physiological signals. Applied Sciences, 15(21), 11777. https://doi.org/10.3390/app152111777 DOI: https://doi.org/10.3390/app152111777
Hasanpoor Reiss, A., & Stricker, D. (2019). Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters, 119, 3–11. https://doi.org/10.1016/j.patrec.2018.02.010 DOI: https://doi.org/10.1016/j.patrec.2018.02.010
Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., & Van Laerhoven, K. (2018). Introducing WESAD, a multimodal dataset for wearable stress and affect detection. In Proceedings of the 20th ACM International Conference on Multimodal Interaction (pp. 400–408). https://doi.org/10.1145/3242969.3242985 DOI: https://doi.org/10.1145/3242969.3242985
Xiang, J., Liu, Z., & Wang, P. (2025). Multimodal stress detection using parallel convolutional neural networks with time–frequency feature fusion. Frontiers in Physiology, 16, 1584299. https://doi.org/10.3389/fphys.2025.1584299 DOI: https://doi.org/10.3389/fphys.2025.1584299
Yang, H., Chen, L., & Zhao, Y. (2025). Stress detection using two-dimensional transformation of physiological signals and deep learning. Scientific Reports, 15(1), 1228. https://doi.org/10.1038/s41598-025-01228-3 DOI: https://doi.org/10.1038/s41598-025-01228-3
Yildirim, O., et al. (2023). Deep learning for ECG-based stress detection: A review. Biomedical Signal Processing and Control, 80, 104319. https://doi.org/10.1016/j.bspc.2022.104319 DOI: https://doi.org/10.1016/j.bspc.2022.104319
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